def __init__(self, opt):
BaseModel.__init__(self, opt) # call the initialization method of BaseModel
# specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images.
self.visual_names = []
# specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks.
# you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them.
# define networks; you can use opt.isTrain to specify different behaviors for training and test.
self.net_names = ["main"]
self.net_main = create_net(opt)
self.loss_criterion = create_loss(opt)
self.init_optimizers(opt)
self.loss_names = getattr(self.loss_criterion, "loss_names")
"""
unfreeze shared_cnn_layers, shared_fc_layers and other_layers,
calculate other loss
not backward
"""
self.plus_other_loss = True
self.need_backward = False
self.max_step = 1
def loss(self):
losses = {}
loss_weights = {}
for i, (loss, data) in enumerate(self.losses.items()):
losses.update(
{loss: create_loss(data["func"], **data.get("kwargs", {}))})
loss_weights.update({loss: data.get("weight", 1)})
return losses, loss_weights
class JointTrainModel(BaseModel):
@staticmethod
def modify_commandline_options(parser):
"""Add new dataset-specific options, and rewrite default values for existing options.
num_classes is the number of classes per task
for example, num_classes = [10,10,10], means the number of classes on taks1 is 10, and then so on.
Parameters:
parser -- original option parser
Returns:
the modified parser.
"""
parser.add_argument('--net_name', type=str, default="alexnet", choices=["alexnet", "imagenet"],
help='network select from alexnet|imagenet', )
parser.add_argument('--loss_name', type=str, default="total", choices=["total"],
help='loss select from total', )
parser.add_argument('--taskdataset_name', type=str, default="total", choices=["total"],
help='loss from total', )
return parser
@staticmethod
def default_value(opt):
return opt
def __init__(self, opt):
BaseModel.__init__(self, opt) # call the initialization method of BaseModel
# specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images.
self.visual_names = []
# specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks.
# you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them.
# define networks; you can use opt.isTrain to specify different behaviors for training and test.
self.net_names = ["main"]
self.net_main = create_net(opt)
self.loss_criterion = create_loss(opt)
self.init_optimizers(opt)
self.loss_names = getattr(self.loss_criterion, "loss_names")
"""
unfreeze shared_cnn_layers, shared_fc_layers and other_layers,
calculate other loss
not backward
"""
self.plus_other_loss = False
self.need_backward = False
self.max_step = self.nb_tasks
if args.arch == 'ssd300':
config = cfg['SSD300']
else:
config = cfg['SSD512']
default_boxes = generate_default_boxes(config)
dataloader, info = create_dataloader(args.data_dir, args.batch_size,
config['image_size'], default_boxes,
args.augmentation, args.num_examples)
ssd = create_ssd(NUM_CLASSES, args.arch, 'base', args.pretrained_path)
ssd.to(device)
criterion = create_loss(args.neg_ratio, NUM_CLASSES)
optimizer = optim.SGD(ssd.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(args.num_epochs * 0.65),
int(args.num_epochs * 0.8)],
gamma=0.1,
last_epoch=-1)
for epoch in range(args.num_epochs):
scheduler.step()
avg_loss = 0.0
